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Climate Variability and Prediction in the Little Colorado River Basin Matt Switanek 1 1 Department of Hydrology and Water Resources University of Arizona
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Predictive Capacity Area of Study Little Colorado River Basin –69,400 km 2 –Average yearly discharge 1.98*10 8 m 3 /year (161,000 acre*ft/year)
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Data Temperature and Precipitation –1/8 degree interpolated data set for the contiguous US –505 gridcells in the Little Colorado Sea Surface Temperature (SST) –International Comprehensive Ocean Atmosphere Data Set –2 degree resolution at a monthly timestep
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Data Preparation Used daily basin (spatial) averages of temperature and precipitation to obtain –Monthly average temperatures –Monthly sums of precipitation Monthly SSTs were spatially averaged over 20° longitude by 10° latitude windows –Initially smooth the data and help fill in spatial and temporal gaps
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Principal Components Analysis Performed on SSTs for each month independently, considering the domain in the Pacific of 125:2:289 E, -44:2:56 N Obtained 12 sets of Principal Component time series.
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Statistical Significance
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January SST Anomalies: Spatial Map
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July SST Anomalies: Spatial Map
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Conditioning First, correlate January SST’s PC’s 1 & 2 average with seasonal precipitation (JFM, FMA …) to get an idea of the strength of correlation between the two time series Next, observe the distribution of precipitation when PC’s 1 & 2 average exceeds a threshold value (.6) Perform a difference of means test to observe how confident we are that the distribution of precipitation data that meets the condition and the distribution that does not are from different populations
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All (std dev) All (mm) Conditioned (std dev) Conditioned (mm) 25%-.6830.22-1.2919.95 50%045.49-.7129.65 75%.6865.24-.1342.23
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SSTs Correlated with Precipitation
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Moving Window Correlations vs. Principal Components
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